library(tidyverse)
library(highcharter)

glimpse(pokemon)

Observations: 718
Variables: 20
$ id              <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 1...
$ pokemon         <chr> "bulbasaur", "ivysaur", "ven...
$ species_id      <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 1...
$ height          <int> 7, 10, 20, 6, 11, 17, 5, 10,...
$ weight          <int> 69, 130, 1000, 85, 190, 905,...
$ base_experience <int> 64, 142, 236, 62, 142, 240, ...
$ type_1          <chr> "grass", "grass", "grass", "...
$ type_2          <chr> "poison", "poison", "poison"...
$ attack          <int> 49, 62, 82, 52, 64, 84, 48, ...
$ defense         <int> 49, 63, 83, 43, 58, 78, 65, ...
$ hp              <int> 45, 60, 80, 39, 58, 78, 44, ...
$ special_attack  <int> 65, 80, 100, 60, 80, 109, 50...
$ special_defense <int> 65, 80, 100, 50, 65, 85, 64,...
$ speed           <int> 45, 60, 80, 65, 80, 100, 43,...
$ color_1         <chr> "#78C850", "#78C850", "#78C8...
$ color_2         <chr> "#A040A0", "#A040A0", "#A040...
$ color_f         <chr> "#81A763", "#81A763", "#81A7...
$ egg_group_1     <chr> "monster", "monster", "monst...
$ egg_group_2     <chr> "plant", "plant", "plant", "...
$ url_image       <chr> "1.png", "2.png", "3.png", "...

hchart works like ggplot2's qplot.
hc_add_series works like ggplot2's geom_S.
hcaes works like ggplot2's aes.

pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n"))

pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "column", hcaes(x = "type_1", y = "n"))

pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "treemap", hcaes(x = "type_1", value = "n", color = "n"))

highchart() %>%
  hc_add_series(pokemon, "scatter", hcaes(x = "height", y = "weight"))

highchart() %>%
  hc_chart(type = "area") %>%
  hc_title(text = "Historic and Estimated Worldwide Population Distribution by Region") %>%
  hc_subtitle(text = "Source: Wikipedia.org") %>%
  hc_xAxis(categories = c("1750", "1800", "1850", "1900", "1950", "1999", "2050"),
           tickmarkPlacement = "on",
           title = list(enabled = FALSE)) %>%
  hc_yAxis(title = list(text = "Percent")) %>%
  hc_tooltip(pointFormat = "<span style=\"color:{series.color}\">{series.name}</span>:
             <b>{point.percentage:.1f}%</b> ({point.y:,.0f} millions)<br/>",
             shared = TRUE) %>%
  hc_plotOptions(area = list(
     stacking = "percent",
     lineColor = "#ffffff",
     lineWidth = 1,
     marker = list(
       lineWidth = 1,
       lineColor = "#ffffff"
       ))
     ) %>%
  hc_add_series(name = "Asia", data = c(502, 635, 809, 947, 1402, 3634, 5268)) %>%
  hc_add_series(name = "Africa", data = c(106, 107, 111, 133, 221, 767, 1766)) %>%
  hc_add_series(name = "Europe", data = c(163, 203, 276, 408, 547, 729, 628)) %>%
  hc_add_series(name = "America", data = c(18, 31, 54, 156, 339, 818, 1201)) %>%
  hc_add_series(name = "Oceania", data = c(2, 2, 2, 6, 13, 30, 46))

y <- quantmod::getSymbols("AMZN", auto.assign = FALSE)

highchart(type = "stock") %>%
  hc_add_series(x) %>%
  hc_add_series(y, type = "ohlc")

hcmap("https://code.highcharts.com/mapdata/countries/in/in-all.js")%>%
  hc_title(text = "India")

trying URL 'https://code.highcharts.com/mapdata/countries/in/in-all.js'
Content type 'application/x-javascript' length 52341 bytes (51 KB)
==================================================
downloaded 51 KB

hcmap("https://code.highcharts.com/mapdata/countries/tr/tr-all.js")%>%
  hc_title(text = "Turkey")

trying URL 'https://code.highcharts.com/mapdata/countries/tr/tr-all.js'
Content type 'application/x-javascript' length 80597 bytes (78 KB)
==================================================
downloaded 78 KB

download_map_data: Download the geojson data from the highcharts collection.
get_data_from_map: Get the properties for each region in the map, as the keys from the map data.

glimpse(mapdata)

Observations: 34
Variables: 20
$ `hc-group`    <chr> "admin1", "admin1", "admin1", "admin1", "admin1", "admin1", "admin1", "admin1", "admin1", "admin1"...
$ `hc-middle-x` <dbl> 0.65, 0.59, 0.50, 0.56, 0.46, 0.46, 0.51, 0.59, 0.47, 0.56, 0.60, 0.63, 0.51, 0.53, 0.53, 0.51, 0....
$ `hc-middle-y` <dbl> 0.81, 0.63, 0.74, 0.38, 0.64, 0.51, 0.34, 0.41, 0.60, 0.32, 0.47, 0.55, 0.48, 0.38, 0.39, 0.46, 0....
$ `hc-key`      <chr> "in-py", "in-ld", "in-wb", "in-or", "in-br", "in-sk", "in-ct", "in-tn", "in-mp", "in-2984", "in-ga...
$ `hc-a2`       <chr> "PY", "LD", "WB", "OR", "BR", "SK", "CT", "TN", "MP", "GU", "GA", "NL", "MN", "AR", "MZ", "TR", "D...
$ labelrank     <chr> "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "9", "2", "9"...
$ hasc          <chr> "IN.PY", "IN.LD", "IN.WB", "IN.OR", "IN.BR", "IN.SK", "IN.CT", "IN.", "IN.MP", "IN.", "IN.GA", "IN...
$ `alt-name`    <chr> "Pondicherry|Puduchcheri|Pondichéry", "Íles Laquedives|Laccadive|Minicoy and Amindivi Islands|Lacc...
$ `woe-id`      <chr> "20070459", "2345748", "2345761", "2345755", "2345742", "2345762", "20070464", "2345758", "2345749...
$ fips          <chr> "IN22", "IN14", "IN28", "IN21", "IN34", "IN29", "IN37", "IN22", "IN35", "IN32", "IN08", "IN20", "I...
$ `postal-code` <chr> "PY", "LD", "WB", "OR", "BR", "SK", "CT", "TN", "MP", NA, "GA", "NL", "MN", "AR", "MZ", "TR", NA, ...
$ name          <chr> "Puducherry", "Lakshadweep", "West Bengal", "Orissa", "Bihar", "Sikkim", "Chhattisgarh", "Tamil Na...
$ country       <chr> "India", "India", "India", "India", "India", "India", "India", "India", "India", "India", "India",...
$ `type-en`     <chr> "Union Territory", "Union Territory", "State", "State", "State", "State", "State", "State", "State...
$ region        <chr> "South", "South", "East", "East", "East", "East", "Central", "South", "Central", "West", "West", "...
$ longitude     <chr> "79.7758", "72.7811", "87.7289", "84.4341", "85.8134", "88.4482", "82.3069", "78.2704", "78.421400...
$ `woe-name`    <chr> "Puducherry", "Lakshadweep", "West Bengal", "Orissa", "Bihar", "Sikkim", "Chhattisgarh", "Tamil Na...
$ latitude      <chr> "10.9224", "11.2249", "23.0523", "20.625", "25.6853", "27.5709", "21.8044", "11.0159", "22.9404", ...
$ `woe-label`   <chr> "Puducherry, IN, India", "Lakshadweep, IN, India", "West Bengal, IN, India", "Orissa, IN, India", ...
$ type          <chr> "Union Territor", "Union Territor", "State", "State", "State", "State", "State", "State", "State",...

hcmap("https://code.highcharts.com/mapdata/countries/in/in-all.js", data = State_pop, value = "Population",
      joinBy = c("hc-a2", "State"), name = "Fake data",
      dataLabels = list(enabled = TRUE, format = '{point.name}'),
      borderColor = "#FAFAFA", borderWidth = 0.1,
      tooltip = list(valueDecimals = 0))

trying URL 'https://code.highcharts.com/mapdata/countries/in/in-all.js'
Content type 'application/x-javascript' length 52341 bytes (51 KB)
==================================================
downloaded 51 KB

df <- data_frame(
  name = c("Animals", "Fruits", "Cars"),
  y = c(5, 2, 4),
  drilldown = tolower(name)
)


ds <- list_parse(df)
names(ds) <- NULL


hc <- highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "Basic drilldown") %>%
  hc_xAxis(type = "category") %>%
  hc_legend(enabled = FALSE) %>%
  hc_plotOptions(
    series = list(
      boderWidth = 0,
      dataLabels = list(enabled = TRUE)
    )
  ) %>%
  hc_add_series(
    name = "Things",
    colorByPoint = TRUE,
    data = ds
  )

dfan <- data_frame(
  name = c("Cats", "Dogs", "Cows", "Sheep", "Pigs"),
  value = c(4, 3, 1, 2, 1)
)

dffru <- data_frame(
  name = c("Apple", "Organes"),
  value = c(4, 2)
)

dfcar <- data_frame(
  name = c("Toyota", "Opel", "Volkswage"),
  value = c(4, 2, 2)
)

second_el_to_numeric <- function(ls){

  map(ls, function(x){
    x[[2]] <- as.numeric(x[[2]])
    x
  })

}

dsan <- second_el_to_numeric(list_parse2(dfan))

dsfru <- second_el_to_numeric(list_parse2(dffru))

dscar <- second_el_to_numeric(list_parse2(dfcar))

hc %>%
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series = list(
      list(
        id = "animals",
        data = dsan
      ),
      list(
        id = "fruits",
        data = dsfru
      ),
      list(
        id = "cars",
        data = dscar
      )
    )
  )

NA

tm <- pokemon %>%
  mutate(type_2 = ifelse(is.na(type_2), paste("only", type_1), type_2),
         type_1 = type_1) %>%
  group_by(type_1, type_2) %>%
  summarise(n = n()) %>%
  ungroup() %>%
  treemap::treemap(index = c("type_1", "type_2"),
                   vSize = "n", vColor = "type_1")

tm$tm <- tm$tm %>%
  tbl_df() %>%
  left_join(pokemon %>% select(type_1, type_2, color_f) %>% distinct(), by = c("type_1", "type_2")) %>%
  left_join(pokemon %>% select(type_1, color_1) %>% distinct(), by = c("type_1")) %>%
  mutate(type_1 = paste0("Main ", type_1),
         color = ifelse(is.na(color_f), color_1, color_f))

Column `type_1` joining factor and character vector, coercing into character vectorColumn `type_2` joining factor and character vector, coercing into character vector

highchart() %>%
  hc_add_series_treemap(tm, allowDrillToNode = TRUE,
                        layoutAlgorithm = "squarified")

'hc_add_series_treemap' is deprecated.
Use 'hctreemap' instead.
See help("Deprecated")

pokemon%>%
  count(type_1)%>%
  arrange(n)%>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n", color = "type_1"))%>%
  hc_exporting(enabled = TRUE)

pokemon%>%
  count(type_1)%>%
  arrange(n)%>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n", color = "type_1"))%>%
  hc_exporting(enabled = TRUE)%>%
hc_add_theme(hc_theme_chalk())

data("weather")

x <- c("Min", "Mean", "Max")
y <- sprintf("{point.%s}", c("min_temperaturec", "mean_temperaturec", "max_temperaturec"))
tltip <- tooltip_table(x, y)

hchart(weather, type = "columnrange",
       hcaes(x = "date", low = "min_temperaturec", high = "max_temperaturec",
             color = "mean_temperaturec")) %>%
  hc_chart(polar = TRUE) %>%
  hc_yAxis( max = 30, min = -10, labels = list(format = "{value} C"),
            showFirstLabel = FALSE) %>%
  hc_xAxis(
    title = list(text = ""), gridLineWidth = 0.5,
    labels = list(format = "{value: %b}")) %>%
  hc_tooltip(useHTML = TRUE, pointFormat = tltip,
             headerFormat = as.character(tags$small("{point.x:%d %B, %Y}")))

NA

devtools::install_github("hrbrmstr/taucharts")
# githubinstall::githubinstall("taucharts")

library(taucharts)

Error in library(taucharts) : there is no package called ‘taucharts’

mtcars2 <- mtcars %>% 
  select(wt, mpg) %>% 
  mutate(model = row.names(mtcars))

taucharts::tauchart(mtcars2) %>% 
  tau_point(x = "wt", y = "mpg") %>% 
  tau_tooltip() %>% 
  tau_trendline()

ggplot(diamonds, aes(x=carat))  # if only X-axis is known. The Y-axis can be specified in respective geoms.

ggplot(diamonds, aes(x=carat, y=price))  # if both X and Y axes are fixed for all layers.

ggplot(diamonds, aes(x=carat, color=cut))  # Each category of the 'cut' variable will now have a distinct  color, once a geom is added.

ggplot(diamonds, aes(x=carat), color="steelblue")

ggplot(diamonds, aes(x=carat, y=price, color=cut)) + 
  geom_point() + 
  geom_smooth()

# Adding scatterplot geom (layer1) and smoothing geom (layer2).

ggplot(diamonds) + 
  geom_point(aes(x=carat, y=price, color=cut)) + 
  geom_smooth(aes(x=carat, y=price, color=cut))

# Same as above but specifying the aesthetics inside the geoms.

library(ggplot2)
ggplot(diamonds) + 
  geom_point(aes(x=carat, y=price, color=cut)) + 
  geom_smooth(aes(x=carat, y=price)) # Remove color from geom_smooth

ggplot(diamonds, aes(x=carat, y=price)) + 
  geom_point(aes(color=cut)) + 
  geom_smooth()  # same but simpler

---
title: "Graphs"
output: html_notebook
---


# RColorBrewer

How to expand color palette with ggplot and RColorBrewer

https://www.r-bloggers.com/how-to-expand-color-palette-with-ggplot-and-rcolorbrewer/



# highcharter

http://jkunst.com/highcharter/

https://github.com/jbkunst/highcharter

http://www.htmlwidgets.org/index.html

https://cran.r-project.org/web/packages/highcharter/index.html

https://www.datacamp.com/community/tutorials/data-visualization-highcharter-r


```{r}
library(tidyverse)
library(highcharter)

```

```{r}
data("pokemon")
glimpse(pokemon)
```






```
hchart works like ggplot2's qplot.
hc_add_series works like ggplot2's geom_S.
hcaes works like ggplot2's aes.
```





```{r}
pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n"))
```


```{r}
pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "column", hcaes(x = "type_1", y = "n"))
```



```{r}
pokemon %>%
  count(type_1) %>%
  arrange(n) %>%
  hchart(type = "treemap", hcaes(x = "type_1", value = "n", color = "n"))
```


```{r}
highchart() %>%
  hc_add_series(pokemon, "scatter", hcaes(x = "height", y = "weight"))
```




```{r}
data(diamonds, package = "ggplot2")

set.seed(123)
data <- sample_n(diamonds, 300)

modlss <- loess(price ~ carat, data = data)
fit <- arrange(broom::augment(modlss), carat)

highchart() %>%
  hc_add_series(data, type = "scatter",
                hcaes(x = "carat", y = "price", size = "depth", group = "cut")) %>%
  hc_add_series(fit, type = "line", hcaes(x = "carat", y = ".fitted"),
                name = "Fit", id = "fit") %>%
  hc_add_series(fit, type = "arearange",
                hcaes(x = "carat", low = ".fitted - 2*.se.fit",
                      high = ".fitted + 2*.se.fit"),
                linkedTo = "fit")


```




```{r}
highchart() %>%
  hc_chart(type = "area") %>%
  hc_title(text = "Historic and Estimated Worldwide Population Distribution by Region") %>%
  hc_subtitle(text = "Source: Wikipedia.org") %>%
  hc_xAxis(categories = c("1750", "1800", "1850", "1900", "1950", "1999", "2050"),
           tickmarkPlacement = "on",
           title = list(enabled = FALSE)) %>%
  hc_yAxis(title = list(text = "Percent")) %>%
  hc_tooltip(pointFormat = "<span style=\"color:{series.color}\">{series.name}</span>:
             <b>{point.percentage:.1f}%</b> ({point.y:,.0f} millions)<br/>",
             shared = TRUE) %>%
  hc_plotOptions(area = list(
     stacking = "percent",
     lineColor = "#ffffff",
     lineWidth = 1,
     marker = list(
       lineWidth = 1,
       lineColor = "#ffffff"
       ))
     ) %>%
  hc_add_series(name = "Asia", data = c(502, 635, 809, 947, 1402, 3634, 5268)) %>%
  hc_add_series(name = "Africa", data = c(106, 107, 111, 133, 221, 767, 1766)) %>%
  hc_add_series(name = "Europe", data = c(163, 203, 276, 408, 547, 729, 628)) %>%
  hc_add_series(name = "America", data = c(18, 31, 54, 156, 339, 818, 1201)) %>%
  hc_add_series(name = "Oceania", data = c(2, 2, 2, 6, 13, 30, 46))
```




```{r}
x <- quantmod::getSymbols("GOOG", auto.assign = FALSE)

hchart(x)
```




```{r}
y <- quantmod::getSymbols("AMZN", auto.assign = FALSE)

highchart(type = "stock") %>%
  hc_add_series(x) %>%
  hc_add_series(y, type = "ohlc")
```

Highmaps - Map Collection  
https://code.highcharts.com/mapdata/  



```{r}
hcmap("https://code.highcharts.com/mapdata/countries/in/in-all.js")%>%
  hc_title(text = "India")
```



```{r}
hcmap("https://code.highcharts.com/mapdata/countries/tr/tr-all.js")%>%
  hc_title(text = "Turkey")
```

```
download_map_data: Download the geojson data from the highcharts collection.
get_data_from_map: Get the properties for each region in the map, as the keys from the map data.
```



```{r}
mapdata <- get_data_from_map(download_map_data("https://code.highcharts.com/mapdata/countries/in/in-all.js"))
glimpse(mapdata)
```




```{r}

#population state wise
pop <-  as.data.frame(c(84673556, 1382611, 31169272, 103804637, 1055450, 25540196, 342853, 242911, 18980000, 1457723, 60383628, 25353081, 6864602,
12548926, 32966238, 61130704, 33387677, 64429, 72597565, 112372972, 2721756, 2964007, 1091014, 1980602, 41947358, 1244464,
27704236, 68621012, 607688, 72138958, 3671032, 207281477, 10116752,91347736))

state <-  mapdata%>%
  select(`hc-a2`)%>%
  arrange(`hc-a2`)

State_pop <-  as.data.frame(c(state, pop))
names(State_pop)= c("State", "Population")

hcmap("https://code.highcharts.com/mapdata/countries/in/in-all.js", data = State_pop, value = "Population",
      joinBy = c("hc-a2", "State"), name = "Fake data",
      dataLabels = list(enabled = TRUE, format = '{point.name}'),
      borderColor = "#FAFAFA", borderWidth = 0.1,
      tooltip = list(valueDecimals = 0))

```




```{r}
data(mpg, package = "ggplot2")

mpgg <- mpg %>%
  filter(class %in% c("suv", "compact", "midsize")) %>%
  group_by(class, manufacturer) %>%
  summarize(count = n())

categories_grouped <- mpgg %>%
  group_by(name = class) %>%
  do(categories = .$manufacturer) %>%
  list_parse()

highchart() %>%
  hc_xAxis(categories = categories_grouped) %>%
  hc_add_series(data = mpgg, type = "bar", hcaes(y = "count", color = "manufacturer"),
                showInLegend = FALSE)
```








```{r}
df <- data_frame(
  name = c("Animals", "Fruits", "Cars"),
  y = c(5, 2, 4),
  drilldown = tolower(name)
)


ds <- list_parse(df)
names(ds) <- NULL


hc <- highchart() %>%
  hc_chart(type = "column") %>%
  hc_title(text = "Basic drilldown") %>%
  hc_xAxis(type = "category") %>%
  hc_legend(enabled = FALSE) %>%
  hc_plotOptions(
    series = list(
      boderWidth = 0,
      dataLabels = list(enabled = TRUE)
    )
  ) %>%
  hc_add_series(
    name = "Things",
    colorByPoint = TRUE,
    data = ds
  )

dfan <- data_frame(
  name = c("Cats", "Dogs", "Cows", "Sheep", "Pigs"),
  value = c(4, 3, 1, 2, 1)
)

dffru <- data_frame(
  name = c("Apple", "Organes"),
  value = c(4, 2)
)

dfcar <- data_frame(
  name = c("Toyota", "Opel", "Volkswage"),
  value = c(4, 2, 2)
)

second_el_to_numeric <- function(ls){

  map(ls, function(x){
    x[[2]] <- as.numeric(x[[2]])
    x
  })

}

dsan <- second_el_to_numeric(list_parse2(dfan))

dsfru <- second_el_to_numeric(list_parse2(dffru))

dscar <- second_el_to_numeric(list_parse2(dfcar))

hc %>%
  hc_drilldown(
    allowPointDrilldown = TRUE,
    series = list(
      list(
        id = "animals",
        data = dsan
      ),
      list(
        id = "fruits",
        data = dsfru
      ),
      list(
        id = "cars",
        data = dscar
      )
    )
  )

```






```{r}
tm <- pokemon %>%
  mutate(type_2 = ifelse(is.na(type_2), paste("only", type_1), type_2),
         type_1 = type_1) %>%
  group_by(type_1, type_2) %>%
  summarise(n = n()) %>%
  ungroup() %>%
  treemap::treemap(index = c("type_1", "type_2"),
                   vSize = "n", vColor = "type_1")

tm$tm <- tm$tm %>%
  tbl_df() %>%
  left_join(pokemon %>% select(type_1, type_2, color_f) %>% distinct(), by = c("type_1", "type_2")) %>%
  left_join(pokemon %>% select(type_1, color_1) %>% distinct(), by = c("type_1")) %>%
  mutate(type_1 = paste0("Main ", type_1),
         color = ifelse(is.na(color_f), color_1, color_f))

highchart() %>%
  hc_add_series_treemap(tm, allowDrillToNode = TRUE,
                        layoutAlgorithm = "squarified")
```



```{r}
pokemon%>%
  count(type_1)%>%
  arrange(n)%>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n", color = "type_1"))%>%
  hc_exporting(enabled = TRUE)
```




```{r}
pokemon%>%
  count(type_1)%>%
  arrange(n)%>%
  hchart(type = "bar", hcaes(x = "type_1", y = "n", color = "type_1"))%>%
  hc_exporting(enabled = TRUE)%>%
hc_add_theme(hc_theme_chalk())
```






```{r}
data("weather")

x <- c("Min", "Mean", "Max")
y <- sprintf("{point.%s}", c("min_temperaturec", "mean_temperaturec", "max_temperaturec"))
tltip <- tooltip_table(x, y)

hchart(weather, type = "columnrange",
       hcaes(x = "date", low = "min_temperaturec", high = "max_temperaturec",
             color = "mean_temperaturec")) %>%
  hc_chart(polar = TRUE) %>%
  hc_yAxis( max = 30, min = -10, labels = list(format = "{value} C"),
            showFirstLabel = FALSE) %>%
  hc_xAxis(
    title = list(text = ""), gridLineWidth = 0.5,
    labels = list(format = "{value: %b}")) %>%
  hc_tooltip(useHTML = TRUE, pointFormat = tltip,
             headerFormat = as.character(tags$small("{point.x:%d %B, %Y}")))

```



---


# taucharts


https://www.infoworld.com/video/87337/r-tip-how-to-create-easy-interactive-scatter-plots-with-taucharts


---
  
<iframe src="//content.jwplatform.com/players/GkbRp8n7-BIrn9usY.html" width="640" height="360" frameborder="0" scrolling="auto"></iframe>  

---




```{r}
devtools::install_github("hrbrmstr/taucharts")
# githubinstall::githubinstall("taucharts")
```


```{r}
library(tidyverse)
library(taucharts)
data("mtcars")
```


```{r}
mtcars2 <- mtcars %>% 
  select(wt, mpg) %>% 
  mutate(model = row.names(mtcars))
```


```{r}
taucharts::tauchart(mtcars2) %>% 
  tau_point(x = "wt", y = "mpg") %>% 
  tau_tooltip() %>% 
  tau_trendline()
```


# gganimate

https://www.infoworld.com/video/89987/r-tip-animations-in-r


# ggplot2


http://r-statistics.co/ggplot2-Tutorial-With-R.html


```{r}
library(ggplot2)
diamonds
ggplot(diamonds)  # if only the dataset is known.
```


```{r}
ggplot(diamonds, aes(x=carat))  # if only X-axis is known. The Y-axis can be specified in respective geoms.
```


```{r}
ggplot(diamonds, aes(x=carat, y=price))  # if both X and Y axes are fixed for all layers.
```


```{r}
ggplot(diamonds, aes(x=carat, color=cut))  # Each category of the 'cut' variable will now have a distinct  color, once a geom is added.
```


```{r}
ggplot(diamonds, aes(x=carat), color="steelblue")
```



- https://ggplot2.tidyverse.org/reference/


```{r}
ggplot(diamonds, aes(x=carat, y=price, color=cut)) + 
  geom_point() + 
  geom_smooth()
# Adding scatterplot geom (layer1) and smoothing geom (layer2).
```



```{r}
ggplot(diamonds) + 
  geom_point(aes(x=carat, y=price, color=cut)) + 
  geom_smooth(aes(x=carat, y=price, color=cut))
# Same as above but specifying the aesthetics inside the geoms.
```


```{r}
library(ggplot2)
ggplot(diamonds) + 
  geom_point(aes(x=carat, y=price, color=cut)) + 
  geom_smooth(aes(x=carat, y=price)) # Remove color from geom_smooth
ggplot(diamonds, aes(x=carat, y=price)) + 
  geom_point(aes(color=cut)) + 
  geom_smooth()  # same but simpler
```


continue from here
http://r-statistics.co/ggplot2-Tutorial-With-R.html




































